The team introduces RISP, a Reversed ISP network for RGB to RAW mapping. Authors combine model-based [1]} approaches and custom layers for an end-to-end RGB o RAW mapping network. In particular, they utilize Tone Mapping and Lens Correction layers from MBISPLD [1]}, and combine it with a custom Color Shift and a Mosaicing Layer. The method is illustrated in Figure REF . The tone mapping, color shift and lens shading are applied by a point-wise multiplication. The mosaicing layer produces an output of \(\frac{h}{2}\frac{w}{2}\)  with a 2x2 convolution with stride 2.
The method is implemented using Pytorch. The network was trained using Adam for 200 epochs and learning rate \(1e-4\) , and \(250\times 250\)  patches. Authors use a combination of the L1 and VGG19 losses.
We must highlight that this model is the smallest among the proposed ones, the model has only 0.17M parameters, it is fast and able to process 4K images under a second.
Based on internal experiments, authors emphasize that there is no clear benefit in using a computationally heavy network as CycleISP [3]} in comparison to lighter or model-based approaches [1]}. The code will be open-sourced.
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